Web Example: Review previous tests and long-term benchmarks, making note of ‘accidental tests’ such as specific offers or product launches.

Form a Hypothesis

Using your background research, make an informed hypothesis. (Literally, an educated guess.) A good hypothesis will be specific enough to guide your test, and includes what you’re testing and your projected outcome.

For early testing, be sure your hypothesis covers elements you can accurately measure such as analytics information (conversions and element clicks), as well as email metrics (open and click-through-rate).

Avoid ‘softer’ metrics such as ‘comfort,’ ‘trust’ or ‘awareness’ unless you have the time, traffic volume, tools and ginormous budget required to test these accurately.

If you find yourself using a psychometric measure in your hypothesis, pair it with hard measures. ‘Trust’ and ‘comfort,’ may show as improved conversion rate or website engagement in time on site and page depth.

Email Example: Highlighting an offer as the subject line in our newsletter will improve email open rate.

Web Example: Including an ‘infographic’ at the top of our homepage will improve understanding, reducing bounce rate and improving conversion rate.

Design a Conversion Rate Optimization Experiment

Whether you’re using MailChimp subject line tests, or Analytics 360, it’s up to you to build your experiment – the ‘B.’

Remember, your ‘A’ should always be your status quo – the current approach. This ensures relevancy in your results and lays the groundwork for further optimization.

Think clinically. If you send your placebo medication group to watch Netflix and the test medication group to the gym, how will you know if your results were the medication or the gym?

To ensure accuracy in your test, maintain as much similarity between versions as possible, adding only the variable of your test. Note in the sample below with subject lines that each include the same subject, adding only the ‘offer’ test at the lead of the test.

Finally, fight the urge to get caught up in minutia. Following on one of our examples, there are countless types of ‘offers,’ but choose one to start, and know that follow-up testing can go for ‘which offer’ if offers do, indeed, improve conversion rate.

Build the most representative version of your test you can, and run it against your control.

Analyze Results

In the lab, this is riveting and highly time-consuming.

In marketing, this step pretty much meets you automatically.

Thankfully, your testing platform includes all the measurement needed to watch your test in action and analyze the results. MailChimp sends you an email when it’s done, and VWO will let you watch the results in near-real-time while the margin of error slowly slims down to a solid business decision.

Conclude (and Continue!)

Did your hypothesis hold, or is the status quo your top performer? Treat yourself to follow-up testing to push your CRO efforts further!

Can you apply your learning against other areas of the customer journey?

Can you be more specific in your hypothesis to uncover more? Example: What kind of offer improves your open rate?

Can you modify your new approach for further improvement? A video in-place of your infographic? An expiration on your offer?

Follow-up testing can yield valuable extra percentage points, but rarely yields the lift of the initial test as you continue to dive deeper into ‘micro’ details. Keeping detailed notes while testing will allow you to return later once you’ve capitalized on as many ‘macro’ tests as you can.

Share and Swipe

I hope this framework helps you and your team build more impactful tests for your brand. Pass this along to help the internet be a better-converting place for all!